The prototype we describe is a biomedical knowledge base, constructed for a demonstration at Banff WWW2007 , that integrates 15 distinct data sources using currently available Semantic Web technologies such as the W3C standard Web Ontology Language [OWL] and Resource Description Framework [RDF]. This report outlines which resources were integrated, how the knowledge base was constructed using free and open source triple store technology, how it can be queried using the W3C Recommended RDF query language SPARQL [SPARQL], and what resources and inferences are involved in answering complex queries. While the utility of the knowledge base is illustrated by identifying a set of genes involved in Alzheimer's Disease, the approach described here can be applied to any use case that integrates data from multiple domains.
A system and method for organizing knowledge in such a way that humans can find knowledge, learn from it, and add to it as needed is disclosed. The exemplary system has four components: a knowledge base, a learning model and an associated tutor, a set of user tools, and a backend system. The invention also preferably comprises a set of application programming interfaces
(APIs) that allow these components to work together, so that other people can create their own versions of each of the components. In the knowledge web a community of people with knowledge to share put knowledge in the database using the user tools. The knowledge may be in the form of documents or other media, or it may be a descriptor of a book or other physical source. Each piece of knowledge is associated with various types of meta-knowledge about what the knowledge is for, what form it is in, and so on. The information in the knowledge base can be created specifically for the knowledge base, but it can also consist of information converted from other sources, such as scientific documents, books, journals, Web pages, film, video, audio files, and course notes. The initial content of the knowledge web comprises existing curriculum materials, books and journals, and those explanatory pages that are already on the World Wide Web. These existing materials already contain most of the information, examples, problems, illustrations, even lesson plans, that the knowledge web needs. The knowledge base thus represents the core content (online documents or references to online or offline documents); the meta-knowledge that was created at the time of entry; and a number of user annotations and document metadata that accumulate over time about the usefulness of the knowledge, additional user opinions, certifications of its veracity and usefulness, commentary, and connections between various units of knowledge.
The YAGO-NAGA project started in 2006 with the goal of building a conveniently searchable, large-scale, highly accurate knowledge base of common facts in a machine-processible representation.
We have already harvested knowledge about millions of entities and facts about their relationships, from Wikipedia and WordNet with careful integration of these two sources. The resulting knowledge base, coined YAGO, has very high precision and is freely available. The facts are represented as RDF triples, and we have developed methods and prototype systems for querying, ranking, and exploring knowledge. Our search engine NAGA provides ranked answers to queries based on statistical models.
So far, most investments in EI have been driven by a focus on an increase in efficiency and topdown change of business processes in relatively static value chains. Typical deployment of EI has been based on the idea of an enterprise-wide "big bang" transition to a new "best way of working", pre-conceived largely by a corporate elite of engineers and analysts. The resulting system and the related procedures were supposed to enforce this way of working and make sure that the enterprise would reap the benefits (of efficiency) for some time to come, by discouraging subsequent unofficial forms of smaller-scale and/or bottom-up change. This approach was very much enterprise centric and typically weak in accommodating subsequent change. It is however no longer adequate, because enterprises increasingly need to rely on bottom-up initiative, emergence and flexibility, in order to remain competitive. Due to fierce global competition, enterprises can no longer survive with
a focus on efficiency and producing more of the same (for a lower price). Instead, enterprises need to concentrate on value innovation and producing more of not the same (with higher margins). To this end enterprises operate increasingly in dynamic value networks.Therefore EI should be geared towards leveraging creativity, collaboration and change in more dynamic networks to release its full potential as an instrument for value creation. A new objective for EI should be: To stimulate value creation based on innovation and co-creation in a context of networked enterprises that is very much defined bottom-up, by creative, committed workers.
The project KiWi is concerned with knowledge management in Semantic Wikis and funded by the European Commission under the Project Number 211932 in the EU Seventh Framework Programme (FP7). KiWi's objective is to investigate how knowledge management in highly dynamic environments can be supported using Semantic Wiki technologies, and how Semantic Wikis can be improved to satisfy the requirements of knowledge management. For this purpose, KiWi will
* implement an advanced knowledge management system based on the Semantic Wiki IkeWiki and extend it by improved, rule-based reasoning support, information extraction, personalisation, and advanced visualisations and editors
* verify the system on two use cases in the areas of project knowledge management and software knowledge management, with flexible workflow models and specific support for the respective application areas.
understanding this content and making decisions based on it (especially in mission-critical situations) is not just a simple matter of consuming information. To effectively "make sense" of large, heterogeneous, and often unstructured content collections requires:
- efficient, accurate, and context-based ways of extracting, filtering, and summarizing information;
- better and more meaningful ways of organizing, visualizing, and interacting with the information;
- faster, more objective methods for investigating hypotheses, detecting trends or patterns across multiple sources, and otherwise analyzing or interpreting information.
In our work the traditional bipartite model of ontologies is extended with the social dimension, leading to a tripartite model of actors, concepts
and instances.We demonstrate the application of this representation by showing how community-based semantics emerges from this model through
a process of graph transformation. We illustrate ontology emergence by two case studies, an analysis of a large scale folksonomy system and a
novel method for the extraction of community-based ontologies from Web pages.
DynaQ stands for 'Dynamic Queries for document based, personal information spaces'.
The goal of the project is to conceptualise and to develop a prototypical inquiry system to explore the personal information space, that supports the user with the help of the searching paradigm orienteering.
The document-based, personal information space of the user constitutes by all documents on his computer. Included are documents of several file formats (e.g. *.pdf), but also the users emails. Also included are all available meta informations and existing semantic informations according to the documents.
Users that are not aware of the scientific context of the project can just think of DynaQ as an desktop search engine.
The research team Edelweiss aims at offering models, methods and techniques for supporting knowledge management and collaboration in virtual communities interacting with information resources through the Web. This research will result in an ergonomic graph-based and ontology-based platform.
E15 is an experimental architecture that places the power of presentation of web content into the hands of those that use it. Based on a dynamic, interactive OpenGL-based scripting engine, E15 exposes an entirely new face to web content, freely modifiable by each individual user.
KML is a file format used to display geographic data in an Earth browser such as Google Earth, Google Maps, and Google Maps for mobile. KML uses a tag-based structure with nested elements and attributes and is based on the XML standard. All tags are case-sensitive and must be appear exactly as they are listed in the KML Reference. The Reference indicates which tags are optional. Within a given element, tags must appear in the order shown in the Reference.
YAGO is a huge semantic knowledge base. Currently, YAGO knows more than 2 million entities (like persons, organizations, cities, etc.). It knows 20 million facts about these entities. Unlike many other automatically assembled knowledge bases, YAGO has a manually confirmed accuracy of 95%
OntoMat-Annotizer is a user-friendly interactive webpage annotation tool. It supports the user with the task of creating and maintaining ontology-based OWL-markups i.e. creating of OWL-instances, attributes and relationships. It include an ontology browser for the exploration of the ontology and instances and a HTML browser that will display the annotated parts of the text. It is Java-based and provide a plugin interface for extensions. The intended user is the individual annotator i.e., people that want to enrich their web pages with OWL-meta data. Instead of manually annotating the page with a text editor, say, emacs, OntoMat allows the annotator to highlight relevant parts of the web page and create new instances via drag?n?drop interactions. It supports the meta-data creation phase of the lifecycle. It is planned that a future version will contain an information extraction plugin, that offers a wizard which suggest which parts of the text are relevant for annotation. That aspect will help to ease the time-consuming annotation task.
Corrib.org is a cluster project, has been started on the base of cooperation between DERI International and Gdansk University of Technology. So far, it gathers together both open source projects being developed based on that cooperations and people affiliated to different sites.
Croquet is a powerful open source software technology that, in the form of the Croquet Software Developer's Kit (Croquet SDK), can be used by experienced software developers to create and deploy deeply collaborative multi-user online vitual world applications on and across multiple operating systems and devices. Derived from Squeak, the Croquet system features a peer-based messaging protocol that dramatically reduces the need for server infrastructures to support virtual world deployment and makes it easy for software developers to create deeply collaborative applications. Cobalt is a National Science Foundation-sponsored effort to develop an open source virtual world browser and authoring toolkit application based on the Croquet technology.
X-Media addresses the issue of knowledge management in complex distributed environments. It will study, develop and implement large scale methodologies and techniques for knowledge management able to support sharing and reuse of knowledge that is distributed in different media (images, documents and data) and repositories (data bases, knowledge bases, document repositories, etc.). The project started in March 2006 and will last for 4 years. It has a budget topping Euro 13.6M, (9.9M from the EU). 15 partners are involved from UK, Germany, Italy, France, Slovenia, Greece and Norway.
CENDI's vision is to provide its member federal STI agencies a cooperative enterprise where capabilities are shared and challenges are faced together so that the sum of accomplishments is greater than each individual agency can achieve on its own.
CENDI's mission is to help improve the productivity of federal science- and technology-based programs through effective scientific, technical, and related information-support systems. In fulfilling its mission, CENDI agencies play an important role in addressing science- and technology-based national priorities and strengthening U.S. competitiveness.
CENDI is an interagency working group of senior scientific and technical information (STI) managers from 13 U.S. federal agencies
KHarvester (Informal Knowledge Harvester) is a SOA layer which collects RDF data from web pages. It provides REST based Web Services for managing data available on Social Semantic Information Sources (SSIS): semantic blogs, semantic wikis, and JeromeDL (the Social Semantic Digital Library). These Web Services allow saving harvested data in the informal knowledge repository, and providing them in a form of informal Learning Objects (LOs) that are described accroding to LOM (Learning Object Metadata) standard.
Also, IKHarvester is an extension to Didaskon system.
Didaskon (διδάσκω - gr. teach) delivers a framework for composing an on-demand curriculum from existing Learning Objects provided by e-Learning services (formal learning). Moreover, the system derives from SSIS which provide informal knowledge. Then, the selection and work-flow scheduling of Learning Objects is based on the semantically annotated specification of the user's current skills/knowledge (pre-conditions), anticipated resulting skills/knowledge (goal) and technical details of the clients platform.
Sensemaking is the process through which humans put together understanding of related information. Sensemaking has been said to involve changes in cognitive representations during a human information processing task. Collection sensemaking involves understanding a collection of media entities, as a whole. One example of a sensemaking task is to compare the damage from Hurricane Katrina to homes, personal effects, and community buildings in different areas of New Orleans. Connected visual and semantic representations provide perspective to support users involved in collection sensemaking tasks. A zoomable map organizes images based on location at varying scales. Multiscale clusters based on zoom level organize images associated with events. The clusters afford contextualized thumbnail browsing and also maintain uniform information density on the map. Metadata enhances context and memory in the process of collection sensemaking.